http4s-native-image
cl-cuda
http4s-native-image | cl-cuda | |
---|---|---|
1 | 5 | |
28 | 270 | |
- | - | |
10.0 | 0.0 | |
about 4 years ago | almost 3 years ago | |
Shell | Common Lisp | |
- | MIT License |
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http4s-native-image
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Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
Haven't read the full article but not sure it's accurate, the benchmarking script (https://github.com/inner-product/http4s-native-image/blob/ma...) doesn't even use the server VM and missing other basic optimizations you'd use if you were running a Uberjar in production.
cl-cuda
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Why Lisp? (2015)
> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this
this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]
[0] https://github.com/JuliaGPU/CUDA.jl
[1] https://github.com/takagi/cl-cuda
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Hacker News top posts: Aug 14, 2021
A Common Lisp Library to Use Nvidia CUDA\ (0 comments)
- A Common Lisp Library to Use Nvidia CUDA
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Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
What are some alternatives?
JWM - Cross-platform window management and OS integration library for Java
numcl - Numpy clone in Common Lisp
criterium - Benchmarking library for clojure
mgl - Common Lisp machine learning library.
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
skiko - Kotlin MPP bindings to Skia
py4cl - Call python from Common Lisp
hash-array-mapped-trie - A hash array mapped trie implementation in c.
higgsml - The winning solution to the The Higgs Boson Machine Learning Challenge.
rewrite - Automated mass refactoring of source code.